Method and devices for analyte monitoring calibration

ABSTRACT

Methods, systems and devices for providing improved calibration accuracy of continuous and/or in vivo analyte monitoring systems based at least in part on insulin delivery information are provided. Many of the embodiments disclosed herein can determine appropriate conditions for performing a calibration of the analyte sensor in view of the scheduled delivery of insulin or administered insulin amount. One or more other parameters or conditions can also be incorporated to improve calibration accuracy including, for example, the physiological model associated with a patient, meal information, exercise information, activity information, disease information, historical physiological condition information, as well as other types of information. Furthermore, according to some embodiments, calibration of the analyte sensor can be delayed or not performed at all, if appropriate conditions are not met.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/915,646, filed Mar. 8, 2018, which is a continuation of U.S.patent application Ser. No. 14/262,697, filed Apr. 25, 2014, now U.S.Pat. No. 9,936,910, which is a continuation of U.S. patent applicationSer. No. 13/925,691, filed Jun. 24, 2013, now U.S. Pat. No. 8,718,965,which is a continuation of U.S. patent application Ser. No. 12/848,075,filed Jul. 30, 2010, now U.S. Pat. No. 8,478,557, which claims priorityto U.S. Provisional Application No. 61/230,686, filed Jul. 31, 2009, allof which are incorporated herein by reference in their entireties forall purposes.

BACKGROUND

As is known, Type-1 diabetes mellitus condition exists when the betacells 4(3-cells) which produce insulin to counteract the rise in glucoselevels in the blood stream) in the pancreas either die or are unable toproduce a sufficient amount of insulin naturally in response to elevatedglucose levels. It is increasingly common for patients diagnosed withdiabetic conditions to monitor their blood glucose levels usingcommercially available continuous glucose monitoring systems to taketimely corrective actions. Some monitoring systems use sensors thatrequire periodic calibration using a reference glucose measurement (forexample, using an in vitro test strip). The FreeStyle Navigator®Continuous Glucose Monitoring System available from Abbott Diabetes CareInc., of Alameda, Calif. is a continuous glucose monitoring system thatprovides the user with real time glucose level information. Using thecontinuous glucose monitoring system, for example, diabetics are able todetermine when insulin is needed to lower glucose levels or whenadditional glucose is needed to raise the level of glucose.

Further, typical treatment of Type-1 diabetes includes the use ofinsulin pumps that are programmed for continuous delivery of insulin tothe body through an infusion set. The use of insulin pumps to treatType-2 diabetes (where the beta cells in the pancreas do produceinsulin, but an inadequate quantity) has also become more prevalent.Such insulin delivery devices are preprogrammed with delivery rates suchas basal profiles which are tailored to each user, and configured toprovide the needed insulin to the user. In addition, continuous glucosemonitoring systems have been developed to allow real time monitoring offluctuation in glucose levels.

When the insulin delivery system and the glucose monitoring system areused separately, used together, or integrated into a single system, forexample, in a single semi-closed loop or closed loop therapy system, theadministered insulin (as well as other parameters or conditions) mayaffect some functions associated with the glucose monitoring system.

SUMMARY

In view of the foregoing, in aspects of the present disclosure, thereare provided methods and apparatus for improving accuracy of thecontinuous glucose monitoring system calibration based at least in parton the insulin delivery information, and parameters associated with theadministration of insulin.

Also provided are systems and kits.

INCORPORATION BY REFERENCE

The following patents, applications and/or publications are incorporatedherein by reference for all purposes: U.S. Pat. Nos. 4,545,382;4,711,245; 5,262,035; 5,262,305; 5,264,104; 5,320,715; 5,356,786;5,509,410; 5,543,326; 5,593,852; 5,601,435; 5,628,890; 5,820,551;5,822,715; 5,899,855; 5,918,603; 6,071,391; 6,103,033; 6,120,676;6,121,009; 6,134,461; 6,143,164; 6,144,837; 6,161,095; 6,175,752;6,270,455; 6,284,478; 6,299,757; 6,338,790; 6,377,894; 6,461,496;6,503,381; 6,514,460; 6,514,718; 6,540,891; 6,560,471; 6,579,690;6,591,125; 6,592,745; 6,600,997; 6,605,200; 6,605,201; 6,616,819;6,618,934; 6,650,471; 6,654,625; 6,676,816; 6,730,200; 6,736,957;6,746,582; 6,749,740; 6,764,581; 6,773,671; 6,881,551; 6,893,545;6,932,892; 6,932,894; 6,942,518; 7,041,468; 7,167,818; and 7,299,082;U.S. Patent Published Application Nos. 2004/0186365; 2005/0182306;2006/0025662; 2006/0091006; 2007/0056858; 2007/0068807; 2007/0095661;2007/0108048; 2007/0199818; 2007/0227911; 2007/0233013; 2008/0066305;2008/0081977; 2008/0102441; 2008/0148873; 2008/0161666; 2008/0267823;and 2009/0054748; U.S. patent application Ser. Nos. 11/461,725;12/131,012; 12/242,823; 12/363,712; 12/495,709; 12/698,124; 12/698,129;12/714,439; 12/794,721; and Ser. No. 12/842,013; U.S. ProvisionalApplication No. 61/347,754.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram illustrating an overall system in accordancewith one embodiment of the present disclosure;

FIG. 2 is a flowchart illustrating calibration accuracy improvementroutine in one aspect of the present disclosure;

FIG. 3 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure;

FIG. 4 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure;

FIG. 5 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure;

FIG. 6 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure;

FIG. 7 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure;

FIG. 8 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure;

FIG. 9 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure;

FIG. 10 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure;

FIG. 11 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure;

FIG. 12 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure; and

FIG. 13 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure.

DETAILED DESCRIPTION

Before embodiments of the present disclosure are described, it is to beunderstood that this disclosure is not limited to particular embodimentsdescribed, as such may, of course, vary. It is also to be understoodthat the terminology used herein is for the purpose of describingparticular embodiments only, and is not intended to be limiting, sincethe scope of the present disclosure will be limited only by the appendedclaims.

Where a range of values is provided, it is understood that eachintervening value, to the tenth of the unit of the lower limit unlessthe context clearly dictates otherwise, between the upper and lowerlimit of that range and any other stated or intervening value in thatstated range, is encompassed within the disclosure. The upper and lowerlimits of these smaller ranges may independently be included in thesmaller ranges is also encompassed within the disclosure, subject to anyspecifically excluded limit in the stated range. Where the stated rangeincludes one or both of the limits, ranges excluding either or both ofthose included limits are also included in the disclosure.

Unless defined otherwise, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this disclosure belongs. Although any methods andmaterials similar or equivalent to those described herein can also beused in the practice or testing of the present disclosure, the preferredmethods and materials are now described. All publications mentionedherein are incorporated herein by reference to disclose and describe themethods and/or materials in connection with which the publications arecited.

It must be noted that as used herein and in the appended claims, thesingular forms “a”, “an”, and “the” include plural referents unless thecontext clearly dictates otherwise.

The publications discussed herein are provided solely for theirdisclosure prior to the filing date of the present application. Nothingherein is to be construed as an admission that the present disclosure isnot entitled to antedate such publication by virtue of prior disclosure.Further, the dates of publication provided may be different from theactual publication dates which may need to be independently confirmed.

As will be apparent to those of skill in the art upon reading thisdisclosure, each of the individual embodiments described and illustratedherein has discrete components and features which may be readilyseparated from or combined with the features of any of the other severalembodiments without departing from the scope or spirit of the presentdisclosure.

The figures shown herein are not necessarily drawn to scale, with somecomponents and features being exaggerated for clarity.

Generally, embodiments of the present disclosure relate to methods andsystem for providing improved analyte sensor calibration accuracy basedat least in part on the insulin delivery information. In certainembodiments, the present disclosure relates to the continuous and/orautomatic in vivo monitoring of the level of an analyte using an analytesensor, and under one or more control algorithms, determines appropriateor suitable conditions for performing calibration of the analyte sensorin view of the scheduled delivery of insulin or administered insulinamount. While the calibration accuracy of the analyte sensor isdiscussed in conjunction with the insulin delivery information, one ormore other parameters or conditions may be incorporated to improve thecalibration accuracy including, for example but not limited to, thephysiological model associated with the patient using the analytesensor, meal information, exercise information, activity information,disease information, and historical physiological condition information.

Embodiments include medication delivery devices such as externalinfusion pumps, implantable infusion pumps, on-body patch pumps, or anyother processor controlled medication delivery devices that are incommunication with one or more control units which also control theoperation of the analyte monitoring devices. The medication deliverydevices may include one or more reservoirs or containers to hold themedication for delivery in fluid connection with an infusion set, forexample, including an infusion tubing and/or cannula. The cannula may bepositioned so that the medication is delivered to the user or patient ata desired location, such as, for example, in the subcutaneous tissueunder the skin layer of the user.

Embodiments include analyte monitoring devices and systems that includean analyte sensor, at least a portion of which is positionable beneaththe skin of the user, for the in vivo detection of an analyte, such asglucose, lactate, and the like, in a body fluid. Embodiments includewholly implantable analyte sensors and analyte sensors in which only aportion of the sensor is positioned under the skin and a portion of thesensor resides above the skin, e.g., for contact to a transmitter,receiver, transceiver, processor, etc.

A sensor (and/or a sensor insertion apparatus) may be, for example,configured to be positionable in a patient for the continuous orperiodic monitoring of a level of an analyte in a patient's dermalfluid. For the purposes of this description, continuous monitoring andperiodic monitoring will be used interchangeably, unless notedotherwise.

The analyte level may be correlated and/or converted to analyte levelsin blood or other fluids. In certain embodiments, an analyte sensor maybe configured to be positioned in contact with dermal fluid to detectthe level of glucose, which detected glucose may be used to infer theglucose level in the patient's bloodstream. For example, analyte sensorsmay be insertable through the skin layer and into the dermal layer underthe skin surface at a depth of approximately 3 mm under the skin surfaceand containing dermal fluid. Embodiments of the analyte sensors of thesubject disclosure may be configured for monitoring the level of theanalyte over a time period which may range from minutes, hours, days,weeks, months, or longer.

Of interest are analyte sensors, such as glucose sensors, that arecapable of in vivo detection of an analyte for about one hour or more,e.g., about a few hours or more, e.g., about a few days of more, e.g.,about three or more days, e.g., about five days or more, e.g., aboutseven days or more, e.g., about several weeks or at least one month.Future analyte levels may be predicted based on information obtained,e.g., the current analyte level at time, the rate of change of theanalyte, etc. Predictive alarms may notify the control unit (and/or theuser) of predicted analyte levels that may be of concern in advance ofthe analyte level reaching the future level. This enables the controlunit to determine a priori a suitable corrective action and implementsuch corrective action.

FIG. 1 is a block diagram illustrating an overall system in accordancewith one embodiment of the present disclosure. Referring to FIG. 1, inone aspect, the system 100 includes an insulin delivery unit 120 that isconnected to a body 110 of a user or patient to establish a fluid pathto deliver medication such as insulin. In one aspect, the insulindelivery unit 120 may include an infusion tubing fluidly connecting thereservoir of the delivery unit 120 to the body 110 using a cannula witha portion thereof positioned in the subcutaneous tissue of the body 110.

Referring to FIG. 1, the system 100 also includes an analyte monitoringunit 130 that is configured to monitor the analyte level in the body110. As shown in FIG. 1, a control unit 140 is provided to control theoperation of the insulin delivery unit 120 and the analyte monitoringunit 130. In one embodiment, the control unit 140 may be a processorbased control unit having provided therein one or more controlalgorithms to control the operation of the analyte monitoring unit 130and the delivery unit 120. In one aspect, the control unit 140, theanalyte monitoring unit 130 and the delivery unit 120 may be integratedin a single housing. In other embodiments, the control unit 140 may beprovided in the housing of the delivery unit 120 and configured forcommunication (wireless or wired) with the analyte monitoring unit 130.In an alternate embodiment, the control unit may be integrated in thehousing of the analyte monitoring unit 130 and configured forcommunication (wireless or wired) with the delivery unit 120. In yetanother embodiment, the control unit 140 may be a separate component ofthe overall system 100 and configured for communication (wireless orwired) with both the delivery unit 120 and the analyte monitoring unit130.

Referring back to FIG. 1, the analyte monitoring unit 130 may include ananalyte sensor that is transcutaneously positioned through a skin layerof the body 110, and is in signal communication with a compact datatransmitter provided on the skin layer of the body 110 which isconfigured to transmit the monitored analyte level substantially in realtime to the analyte monitoring unit 130 for processing and/or display.In another aspect, the analyte sensor may be wholly implantable in thebody 110 with a data transmitter and configured to wirelessly transmitthe monitored analyte level to the analyte monitoring unit 130.

Referring still to FIG. 1, also shown in the overall system 100 is adata processing device 150 in signal communication with the one or moreof the control unit 140, delivery unit 120 and the analyte monitoringunit 130. In one aspect, the data processing device 150 may include anoptional or supplemental device in the overall system 100 to provideuser input/output functions, data storage and processing. Examples ofthe data processing device 150 include, but are not limited to mobiletelephones, personal digital assistants (PDAs), in vitro blood glucosemeters, smart phone devices including Blackberry® devices, iPhone®devices, and Palm® devices, data paging devices, and the like, each ofwhich include an output unit such as one or more of a display, audibleand/or vibratory output, and/or an input unit such as a keypad,keyboard, input buttons and the like, and which are configured forcommunication (wired or wireless) to receive and/or transmit data, andfurther, which include memory devices such as random access memory, readonly memory, volatile and/or non-volatile memory that store data.

Also shown in the overall system 100 is a data processing terminal 160which may include a personal computer, a server terminal, a laptopcomputer, a handheld computing device, or other similar computingdevices that are configured for data communication (over the internet,local area network (LAN), cellular network and the like) with the one ormore of the control unit 140, the delivery unit 120, the analytemonitoring unit 130, and the data processing device 150, to process,analyze, store, archive, and update information.

It is to be understood that the analyte monitoring unit 130 of FIG. 1may be configured to monitor a variety of analytes at the same time orat different times. Analytes that may be monitored include, but are notlimited to, acetyl choline, amylase, bilirubin, cholesterol, chorionicgonadotropin, creatine kinase (e.g., CK-MB), creatine, DNA,fructosamine, glucose, glutamine, growth hormones, hormones, ketones,lactate, peroxide, prostate-specific antigen, prothrombin, RNA, thyroidstimulating hormone, and troponin. The concentration of drugs, such as,for example, antibiotics (e.g., gentamicin, vancomycin, and the like),digitoxin, digoxin, drugs of abuse, theophylline, and warfarin, may alsobe monitored. In those embodiments that monitor more than one analyte,the analytes may be monitored at the same or different times.

Additional detailed descriptions of embodiments of the continuousanalyte monitoring device and system, calibrations protocols,embodiments of its various components are provided in, among others,U.S. Pat. Nos. 6,175,752, 6,284,478, 7,299,082 and U.S. patentapplication Ser. No. 10/745,878 filed Dec. 26, 2003 entitled “ContinuousGlucose Monitoring System and Methods of Use”, the disclosures of eachof which are incorporated herein by reference in their entirety for allpurposes. Additional detailed description of systems includingmedication delivery units and analyte monitoring devices, embodiments ofthe various components are provided in, among others, U.S. patentapplication Ser. No. 11/386,915, entitled “Method and System forProviding Integrated Medication Infusion and Analyte Monitoring System”,the disclosure of which is incorporated herein by reference for allpurposes. Moreover, additional detailed description of medicationdelivery devices and components are provided in, among others, U.S. Pat.No. 6,916,159, the disclosure of which is incorporated herein byreference for all purposes.

Referring back to FIG. 1, each of the components shown in the system 100may be configured to be uniquely identified by one or more of the othercomponents in the system so that communication conflict may be readilyresolved between the various components, for example, by exchanging orpre-storing and/or verifying unique device identifiers as part ofcommunication between the devices, by using periodic keep alive signals,or configuration of one or more devices or units in the overall systemas a master-slave arrangement with periodic bi-directional communicationto confirm integrity of signal communication therebetween.

Further, data communication may be encrypted or encoded (andsubsequently decoded by the device or unit receiving the data), ortransmitted using public-private keys, to ensure integrity of dataexchange. Also, error detection and/or correction using, for example,cyclic redundancy check (CRC) or techniques may be used to detect and/orcorrect for errors in signals received and/or transmitted between thedevices or units in the system 100. In certain aspects, datacommunication may be responsive to a command or data request receivedfrom another device in the system 100, while some aspects of the overallsystem 100 may be configured to periodically transmit data withoutprompting, such as the data transmitter, for example, in the analytemonitoring unit 130 periodically transmitting analyte related signals.

In certain embodiments, the communication between the devices or unitsin the system 100 may include one or more of an RF communicationprotocol, an infrared communication protocol, a Bluetooth® enabledcommunication protocol, an 802.11x wireless communication protocol,internet connection over a data network or an equivalent wirelesscommunication protocol which would allow secure, wireless communicationof several units (for example, per HIPAA requirements) while avoidingpotential data collision and interference.

In certain embodiments, data processing device 150, analyte monitoringunit 130 and/or delivery unit 120 may include blood glucose meterfunctions or capability to receive blood glucose measurements which maybe used, for example to calibrate the analyte sensor. For example, thehousing of these devices may include a strip port to receive a bloodglucose test strip with blood sample to determine the blood glucoselevel. Alternatively, a user input device such as an input button orkeypad may be provided to manually enter such information. Stillfurther, upon completion of a blood glucose measurement, the result maybe wirelessly and/or automatically transmitted to another device in thesystem 100. For example, it is desirable to maintain a certain level ofwater tight seal on the housing of the delivery unit 120 duringcontinuous use by the patient or user. In such case, incorporating astrip port to receive a blood glucose test strip may be undesirable. Assuch, the blood glucose meter function including the strip port may beintegrated in the housing of another one of the devices or units in thesystem (such as in the analyte monitoring unit 130 and/or dataprocessing device 150). In this case, the result from the blood glucosetest, upon completion may be wirelessly transmitted to the delivery unit120 for storage and further processing.

Any suitable test strip may be employed, e.g., test strips that onlyrequire a very small amount (e.g., one microliter or less, e.g., 0.5microliter or less, e.g., 0.1 microliter or less), of applied sample tothe strip in order to obtain accurate glucose information, e.g.Freestyle® or Precision® blood glucose test strips from Abbott DiabetesCare Inc. Glucose information obtained by the in vitro glucose testingdevice may be used for a variety of purposes, computations, etc. Forexample, the information may be used to calibrate the analyte sensor,confirm results of the sensor to increase the confidence in the accuracylevel thereof (e.g., in instances in which information obtained bysensor is employed in therapy related decisions), determine suitableamount of bolus dosage for administration by the delivery unit 120.

In certain embodiments, a sensor may be calibrated using only one sampleof body fluid per calibration event. For example, a user need only lancea body part one time to obtain sample for a calibration event (e.g., fora test strip), or may lance more than one time within a short period oftime if an insufficient volume of sample is obtained firstly.Embodiments include obtaining and using multiple samples of body fluidfor a given calibration event, where glucose values of each sample aresubstantially similar. Data obtained from a given calibration event maybe used independently to calibrate or combined with data obtained fromprevious calibration events, e.g., averaged including weighted averaged,etc., to calibrate.

One or more devices or components of the system 100 may include an alarmsystem that, e.g., based on information from control unit 140, warns thepatient of a potentially detrimental condition of the analyte. Forexample, if glucose is the analyte, an alarm system may warn a user ofconditions such as hypoglycemia and/or hyperglycemia and/or impendinghypoglycemia, and/or impending hyperglycemia. An alarm system may betriggered when analyte levels reach or exceed a threshold value. Analarm system may also, or alternatively, be activated when the rate ofchange or acceleration of the rate of change in analyte level increaseor decrease reaches or exceeds a threshold rate of change oracceleration. For example, in the case of the glucose monitoring unit130, an alarm system may be activated if the rate of change in glucoseconcentration exceeds a threshold value which might indicate that ahyperglycemic or hypoglycemic condition is likely to occur. In the caseof the delivery unit 120, alarms may be associated with occlusionconditions, low reservoir conditions, malfunction or anomaly in thefluid delivery and the like. System alarms may also notify a user ofsystem information such as battery condition, calibration, sensordislodgment, sensor malfunction, etc. Alarms may be, for example,auditory and/or visual. Other sensory-stimulating alarm systems may beused including alarm systems which heat, cool, vibrate, or produce amild electrical shock when activated.

Referring yet again to FIG. 1, the control unit 140 of the system 100may include one or more processors such as microprocessors and/orapplication specific integrated circuits (ASIC), volatile and/ornon-volatile memory devices, and additional components that areconfigured to store and execute one or more control algorithms todynamically control the operation of the delivery unit 120 and theanalyte monitoring unit 130. The one or more closed loop controlalgorithms may be stored as a set of instructions in the one or morememory devices and executed by the one or more processors to vary theinsulin delivery level based on, for example, glucose level informationreceived from the analyte sensor.

An exemplary model describing the blood-to-interstitial glucose dynamicstaking into account of insulin information is described below. Morespecifically, the model described herein provides for specificelaboration of model-based improvements discussed below. The exampleprovided herein is based on a particular blood-to-interstitial glucosemodel, and while other models may result in a different particularrelationship and parameter set, the underlying concepts and relateddescription remain equally applicable.

Provided below is a model of blood-to-interstitial glucose as describedby Wilinska et al. (Wilinska, Bodenlenz, Chassin, Schaller, Schaupp,Pieber, and Hovorka, “Interstitial Glucose Kinetics in Subjects WithType 1 Diabetes Under Physiologic Conditions”, Metabolism, v. 53 n. 11,Nov. 2004, pp. 1484-1492, the disclosure of which is incorporated hereinby reference), where interstitial glucose dynamics comprises of a zeroorder removal of glucose from interstitial fluid Foe, a constant decayrate constant k₀₂, a constant glucose transport coefficient k₂₁, and aninsulin dependent glucose transport coefficient k_(i).

ġ _(i)(t)=−k ₀₂ g _(i)(t)+[k ₂₁+[k _(i)[I(t)−I _(b)]]]g _(b)(t)−F₀₂  (1)

where g_(i) corresponds to interstitial glucose, g_(b) corresponds toblood glucose, the dot corresponds to the rate of change operation, (t)refers to variables that change over time as opposed to relativelystatic aforementioned coefficients, I corresponds to insulinconcentration at any given time, and I_(b) corresponds to thesteady-state insulin concentration required to maintain a net hepaticglucose balance.

It should be noted that the blood-to-interstitial glucose modeldescribed above is affected by insulin and accordingly, factoring in theinsulin information will provide improvement to the sensor sensitivitydetermination.

The determination of insulin concentration (I) and the steady stateinsulin concentration required to maintain a net hepatic glucose balance(I_(b)) as shown in Equation (1) above may be achieved using insulindosing history and an insulin pharmacokinetic and pharmacodynamic model.For example, based on a three compartment model of subcutaneous insulindynamics into plasma insulin I as described by Hovorka, et al. (Hovorka,Canonico, Chassin, Haueter, Massi-Benedetti, Federici, Pieber, Schaller,Schaupp, Vering and Wilinska, “Nonlinear model predictive control ofglucose concentration in subjects with type 1 diabetes”, PhysiologicalMeasurement, v. 25, 2004, pp. 905-920, the disclosure of which isincorporated herein by reference):

İ ₁(t)=−k _(a) I ₁(t)+u _(sc)(t)

İ ₂(t)=−k _(a) I ₂(t)+k _(a) I ₁

İ(t)=−k _(e) I(t)+k _(a) /V I ₂(t)  (2)

where I₁ and I₂ are internal insulin compartments that describe thepathway from subcutaneous insulin injection into the plasma insulincompartment I. I_(b) is calculated by taking the steady-state average ofI over a finite window of past and present period. The coefficientsk_(a) and k_(e) describe the various decay and transport rates of thecompartments, and Vis the plasma insulin volume. Insulin action time isrelated to the parameter k_(a). The input u_(sc) to this model isdescribed in terms of subcutaneous insulin infusion rate. Insulindose/bolus may be converted into its delivery rate equivalent bymonitoring or estimating the actual amount of bolus amount/dosedelivered after every regular intervals of time (e.g. by monitoring ofthe amount of bolus/dose delivered every minute for a given executedbolus dose delivery).

For analyte monitoring systems, an uncalibrated sensor measurementy_(CGM) is related to the true interstitial glucose by the followingequation:

y _(CGM)(t)=S[g _(i)(t)+v _(i)(t)]  (3)

where S is the calibration sensitivity to be identified, and v_(i) issensor noise.

Further, reference blood glucose measurement y_(BG) when available atcertain times, such as when requested for calibration at time to,contaminated by measurement error v_(b) may be expressed as follows:

y _(BG)(t _(o))=g _(b)(t _(o))+v _(b)(t _(o))  (4)

Accordingly, the models and functional relationships described aboveprovide some exemplary system components for providing improvement tothe calibration accuracy in analyte monitoring systems whether used as astandalone system, or in conjunction with a medication delivery systemsuch as with an insulin pump.

Determination of the suitable or appropriate time period to performsensor calibration routine may be accomplished in several manners withinthe scope of the present disclosure. In one aspect, the calibrationschedule may be predetermined or preset based on the initial sensorinsertion or positioning in the patient or alternatively, scheduledbased on each prior successful calibration event on a relative timebasis. In some aspects, calibration routines are delayed or cancelledduring high rates of glucose fluctuation because physiological lagbetween interstitial glucose measured by the analyte sensor and theblood glucose measured by discrete in vitro test strips may result in anerror in the sensor sensitivity estimation.

In one aspect, calibration routine or function may be prevented orrejected when the interstitial glucose absolute rate of change isdetermined to exceed a predetermined threshold level. As theinterstitial glucose level generally lags blood glucose level, there maybe time periods where the blood glucose may be changing rapidly whilethe measured interstitial glucose level may not report similarfluctuations—it would change rapidly at some later, lagged time period.In such a case, a lag error may be introduced to the sensitivitydetermination. Accordingly, in one aspect, the execution of thecalibration routine may be delayed or postponed when a sensorcalibration request is detected by the system 100 during a time periodwhen an insulin dose of sufficient magnitude is delivered, which maycause the rapid change in blood glucose to occur without a rapid changeof interstitial glucose at that instance.

Referring now to the Figures, FIG. 2 is a flowchart illustrating overallcalibration accuracy improvement routine in one aspect of the presentdisclosure. Referring to FIG. 2, when calibration start event isdetected (210), for example, based on a predetermined calibrationschedule from sensor insertion, or in response to a user calibrationfunction initiation or execution, it is determined whether the initiatedcalibration routine is to be executed based on, for example, insulininformation (220). Thereafter, one or more data or informationassociated with the determination is used to generate an output (230)which may, in one aspect, be provided to the user and/or stored in thesystem 100 (FIG. 1).

FIG. 3 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure. As shown, when thecalibration start event is detected (310), it is determined whether aninsulin dose (for example, a bolus amount such as a carbohydrate bolus,or a correction bolus dose) was delivered or administered to the patient(320). In one aspect, as part of determining whether the insulin dosewas delivered, it may be also determined whether the insulin dose wasdelivered within a time period measured from the detected calibrationstart event (and further, optionally, whether the determined insulindose delivered amount meets a predetermined threshold level of insulin).

Referring again to FIG. 3, if it is determined that the insulin dose wasdelivered, then the routine proceeds to step 340 where the initiatedcalibration routine is not executed, and the routine returns to thebeginning and awaits for the detection of the next or subsequentcalibration start event. On the other hand, if at step 320 it isdetermined that the insulin dose was not delivered, then at step 330,the initiated calibration routine is executed to determine, for example,the corresponding sensor sensitivity based on a contemporaneouslydetermined reference measurement (e.g., blood glucose measurement froman in vitro test strip, or another sensor data point that may be used asreference measurement) to calibrate the sensor.

FIG. 4 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure. Referring to FIG.4, in the embodiment shown, when the calibration start event is detected(410) it is determined whether the insulin on board (JOB) level exceedsa predetermined threshold level (420). That is, in one aspect, thecontrol algorithm may be configured to determine, in response to thedetection of a calibration routine initialization, the IOB level. In oneaspect, if it is determined that the IOB level exceeds the predeterminedthreshold level, then the initiated calibration routine is notcontemporaneously executed (440), but rather, the called routine may bedelayed, postponed, or cancelled, and the routine returns to thebeginning to detect the subsequent calibration start event.

Referring to FIG. 4, if on the other hand it is determined that the IOBlevel is not greater than the predetermined threshold level at step 420,then the initiated calibration routine is executed at step 430 (530(FIG. 5)), as discussed above, for example, to determine thecorresponding analyte sensor sensitivity based on one or more referenceglucose measurements to calibrate the sensor data.

FIG. 5 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure. Compared to theembodiment described in conjunction with FIG. 4, in the embodiment shownin FIG. 5, when the IOB level is determined to exceed the predeterminedthreshold level (520), then again, the initiated calibration routine isnot executed (540), but prior to returning to the beginning of theroutine to detect the subsequent calibration start event (510), a usernotification function is called to notify the user of a failed (ordelayed/postponed) calibration event (550). Such notification mayinclude one or more of a visual indication, an audible indication, avibratory indication, or one or more combinations thereof.

FIG. 6 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure. Referring to FIG.6, in a further aspect of the present disclosure, when a calibrationstart event such as the initialization of a scheduled calibrationroutine is detected (610), it is determined whether a predetermined orcategorized event has been logged at step 620. In particular, thecontrol algorithm may be configured to determine whether an event suchas a meal event, an activity event, an exercise event, or any othersuitable or classified event has been logged at step 620. As discussedabove, in one aspect, the control algorithm may be configured toadditionally determine the time period of when such event was logged, ifany, to determine whether the determined time period falls within arelevant time period with respect to the initiated calibration routine.

For example, if the logged meal event occurred with sufficient temporaldistance relative to the initiated calibration routine, that it likelywill have minimal relevance, if any to the calibration accuracyassociated with the analyte sensor, then such logged event may beignored. Alternatively, with each retrieved logged event at step 620,the routine may be configured to determine whether the logged eventoccurred within a specified or predetermined time period, in which case,the routine proceeds to step 640 where the initiated calibration routineis not executed and/or postponed or delayed. As further shown in FIG. 6,the routine thereafter returns to the beginning and monitors the systemto determine whether a subsequent calibration start event is detected.

Referring back to FIG. 6, if at step 620 there are no events loggedwhich are classified or categorized as relevant or associated with aparameter that is considered to be relevant, or alternatively, the oneor more logged events detected or retrieved fall outside of thepredetermined relevant time period (for example, within one hour priorto the calibration start event detected), then the initiated calibrationroutine proceeds at step 630 and is executed to determine, for example,the sensitivity associated with the analyte sensor based, for example,on a received reference blood glucose measurement, to calibrate thesensor data.

In aspects of the present disclosure, the duration and/or thresholddescribed may be determined based on parameters including, for example,but not limited to insulin sensitivity, insulin action time, time ofday, analyte sensor measured glucose level, glucose rate of change, andthe like.

Moreover, in aspects of the present disclosure, as discussed, if thecondition described above is detected, rather instead of delaying orpostponing the execution of the calibration routine, the sensitivitydetermination may be altered as described in further detail below. Thatis, in one aspect, a correction factor may be applied to the sensitivitydetermination based on the insulin dose amount, elapsed time since theadministration of the insulin dose, insulin sensitivity and insulinaction time, for example. In one aspect, the correction factor may be apredetermined value or parameter, for example, based in part on themodel applied to the patient's physiological condition, or may be afactor that is configured to be dynamically updated in accordance withthe variation in the monitored parameters such as those described above.

In a further aspect, a glucose model of a patient may be used to predictor determine future glucose (blood and/or interstitial) levels and toestimate present glucose levels (blood and/or interstitial). Morespecifically, in aspects of the present disclosure, the model appliedmay also be used to estimate a rate-of-change of these variables andhigher order moments of these variables in addition to statistical errorestimates (for example, uncertainty estimates).

As discussed, the insulin delivery information and the measured glucosedata from the analyte sensor (e.g., multiple measurements of each intime) are two of many input parameters used in conjunction with theembodiments described herein. Accordingly, in one aspect, thecalibration routine may be configured to use the predicted output(s) asa check or verification to determine if the calibration routine shouldbe postponed or delayed. For example, if the rate of change of bloodglucose is determined to exceed a predetermined threshold, thecalibration routine may be postponed or delayed for a predetermined timeperiod. Alternatively, in a further aspect, if it is determined that theuncertainty in the interstitial estimate exceeds a predeterminedthreshold, the calibration routine may be configured to be postponed ordelayed for a predetermined time period. The predetermined time periodfor a delayed or postponed calibration routine may be a preset timeperiod, or alternatively, dynamically modified based on, for example,but not limited to the level of determined uncertainly in theinterstitial estimate, the level of the predetermined threshold, and/orany other relevant parameters or factors monitored or otherwise providedor programmed in the system 100 (FIG. 1).

Referring now again to the Figures, FIG. 7 is a flowchart illustratingcalibration accuracy improvement routine in another aspect of thepresent disclosure. Referring to FIG. 7, in the embodiment shown, whenthe calibration start event is detected at step 710 the routinedetermines one or more physiological model outputs based on one or morepresent and/or past input parameters and values (720) including, forexample, monitored sensor data, insulin delivery information, bloodglucose estimates, blood glucose rate of change estimate values, and thelike. Thereafter, at step 730, it is determined whether the rate ofchange of the estimated glucose level deviates from a predeterminedthreshold (for example, where the estimated rate exceeds a presetpositive value, or the estimated rate falls below a preset negativevalue). If it is determined that the estimated glucose rate of change isnot within the predetermined threshold at step 730, then at step 750,the routine discontinues the calibration function (or postpones ordelays the initiated calibration routine). Thereafter, as shown in FIG.7, the routine returns to the beginning to detect the subsequentcalibration start event at step 710.

Referring still to FIG. 7, if at step 730 it is determined that theestimated glucose rate of change is within the predetermined threshold,then at step 740, the routine proceeds with the execution of thecalibration routine to determine, for example, the sensitivityassociated with the analyte sensor by prompting the user to input areference blood glucose measurement value (for example, based on an invitro blood glucose testing), or the system may be configured toretrieve an existing or contemporaneously received reference measurementdata to determine the sensitivity value for calibrating the sensor data.

FIG. 8 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure. Referring now toFIG. 8, when the calibration start event is detected at step 810 and themodel outputs are determined based on one or more present and/or pastinput parameters or values (820) as discussed above in conjunction withFIG. 7, in the embodiment shown in FIG. 8, the calibration routine isexecuted based, in part on the estimated glucose value and/or thedetermined rate of change of the glucose level (830). That is, in oneembodiment, when the scheduled calibration routine is initiated, theroutine determines the most suitable or accurate parameters or valuesthat are available to proceed with the execution of the calibrationroutine (as compared to determining whether or not the calibrationcondition is appropriate).

FIG. 9 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure. Referring to FIG.9, in one aspect, when the calibration start event is detected at step910, it is thereafter determined when an insulin dose has been deliveredat step 920. That is, when a scheduled calibration routine is called orinitiated, the routine determines whether there has been insulin dosedelivery that may impact the conditions associated with the calibrationof the analyte sensor. For example, in one aspect, the routine maydetermine whether the insulin dose is delivered within a predeterminedtime period measured from the initiation of the calibration routine(step 910) such as, within the past 1-2 hours, for example. That is, thesystem may be configured such that insulin dose administered outsidesuch predetermined time period may be considered not sufficientlysignificant to adversely affect the conditions related to thecalibration of the analyte sensor, and therefore, ignored.

Referring again to FIG. 9, when it is determined that the insulin dosewas delivered (920) for example, during the relevant predetermined timeperiod, the scheduled calibration function is delayed for apredetermined or programmed time period. That is, the scheduledcalibration function is executed after the programmed time period hasexpired at step 940 (such that any potentially adverse affect of thedetected insulin dose delivery (at step 920) has dissipated sufficientlyduring the programmed time period to proceed with the calibrationroutine). On the other hand, if it is determined that there is noinsulin dose delivery detected (920) or any detected insulin dosedelivery falls outside the relevant time period, then at step 930, theinitiated calibration routine is performed as described above. In thismanner, in one aspect of the present disclosure, when insulin doseadministration such as bolus dose administration is detected within arelevant time period during a scheduled or user initiated calibrationroutine, a time delay function is provided to dissipate the effects ofthe administered insulin dose, before calibration routine resumes.

FIG. 10 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure. Referring to FIG.10, in the embodiment shown, upon detection of the calibration startevent 1010, it is determined whether a medication dose (such as insulindose) was delivered (1020) (for example, during a relevant time periodas described above in conjunction with FIG. 9 above). If not, then thecalibration routine is executed to completion at step 1050. On the otherhand, if it is determined that the medication dose was delivered duringthe relevant time period (1020) (for example, within 1-2 hours of thedetected calibration start event), at step 1030, the amount of deliveredmedication dose is compared against a threshold level to determinewhether the delivered medication dose exceeds the threshold level. Ifnot, then the calibration routine is executed or performed to completionas described above at step 1050.

If on the other hand it is determined that the delivered medication doseexceeds the threshold level, then at step 1040, the detected start ofthe calibration event is delayed or postponed for a preprogrammed timeperiod. In one aspect, the preprogrammed time period may be dynamicallyadjusted based on the amount of the medication dose that exceeds thatthreshold level, or alternatively, the preprogrammed time period may bea fixed value. In this manner, in one aspect, when it is determined thatmedication dose was administered contemporaneous to a scheduledcalibration event, the routine may be configured to determine therelevance of the delivered medication dose to modify the calibrationtiming accordingly (for example, to continue with the execution of thecalibration routine or to delay the calibration routine to minimize anypotential adverse effect of the delivered medication dose).

FIG. 11 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure. Referring to FIG.11, in the embodiment shown, when the calibration start event isdetected 1110, a model based on one or more output values is determinedbased on one or more present and/or past input parameters or values(1120) as discussed above in conjunction with FIGS. 7 and 8 above, forexample. It is to be noted that the model based determination asdescribed herein may include one or more physiological models determinedto a particular individual, condition and/or the severity of thecondition or customized for one or more specific applications.

Referring to FIG. 11, after the model based outputs are determined atstep 1120, it its determined whether the determined outputs or estimatesof the outputs are within a predetermined threshold level at step 1130.That is, output parameters or values are determined based on one or morepredetermined model applications relevant to, for example, the glycemicprofile of a patient or a type of patients, and thereafter, thedetermined or estimated output parameters are compared to thepredetermined threshold level. When it is determined that the estimatedoutputs are not within the threshold level, then at step 1150, theinitiated calibration routine is delayed or postponed for apredetermined time period before executing the calibration function tocompletion as described above.

On the other hand, as shown in FIG. 11, if it is determined that theestimated output parameters or values are within the predeterminedthreshold value, the at step 1140, the calibration routine is executed,for example, to determine the sensitivity associated with the analytesensor based on available reference glucose data, and thereaftercalibrating the sensor data.

As discussed, in aspects of the present disclosure, the calibrationaccuracy routines may include other parameters or data such as, forexample, meal intake information. For example, an aspect of thecalibration routine may include confirming or determining whether a mealevent has occurred for example, within the last hour prior to thescheduled calibration event, and further postpone or delay calibrationif it is determined that the consumed meal during the past hour wassufficiently large or greater than a set threshold amount (for example,based on carbohydrate estimate). In one aspect, the meal intakeinformation parameter used in conjunction with the calibration routinemay be performed in conjunction with the insulin dose information asdescribed above, or alternatively, as a separate routine for determiningor improving the timing of performing the calibration routines.

In another aspect, the insulin dose information and/or other appropriateor suitable exogenous data/information may be used to improve the sensorsensitivity determination. For example, in one aspect, a model may beused to account for blood glucose and interstitial glucose, and insulinmeasurement data is used to help compensate for the lag between the two.The model would produce a blood glucose estimate that could be relatedto the reference blood glucose estimate in order to determine thesensitivity. Alternatively, the sensitivity could be part of the modeland estimated. Additional detailed description related to pumpinformation to improve analyte sensor accuracy is provided in U.S.patent application Ser. No. 12/024,101 entitled “Method and System forDetermining Analyte Levels”, the disclosure of which is incorporated byreference for all purposes.

More specifically, referring back to and based on an example of theblood-to-interstitial glucose dynamics model which accounts for insulin,an estimated sensitivity at time to that is a function of availablereference blood glucose (BG) measurement, analyte sensor measurement,and insulin information can be described as below:

$\begin{matrix}{{\hat{S}\left( t_{o} \right)} = \frac{\left\lbrack {{{\overset{.}{y}}_{CGM}\left( t_{o} \right)} + \left\lbrack {k_{02}{y_{CGM}\left( t_{o} \right)}} \right\rbrack} \right\rbrack + F_{02}}{\left\lbrack {k_{21} + \left\lbrack {k_{i}\left\lbrack {{I\left( t_{o} \right)} - I_{b}} \right\rbrack} \right\rbrack} \right\rbrack{y_{BG}\left( t_{o} \right)}}} & (5)\end{matrix}$

It is to be noted that if insulin information is not accounted for, asshown in Equation 5 above, the denominator will be smaller, resulting inthe sensitivity estimate larger than the actual value.

In another aspect, a closed loop control system is contemplated where aportion of the control algorithm seeks not only to prevent glucoseexcursions outside the euglycemic range, but also to provide improvedconditions for calibration. While two particular conditions aredescribed as examples, within the scope of the present disclosure, otherconditions may be contemplated that are suitable or appropriate,depending on the type of analyte sensor used and/or other factors,variables or parameters.

In some cases, two conditions or states generally provide bettercalibration performance (i.e., better accuracy in sensitivityestimate)—calibrating during higher glucose periods and during lowglucose rates-of-change. Calibrating during high glucose episodes isfavorable because some errors tend to be unrelated to glucose level andwill contribute to the sensitivity calculation proportionally less whenglucose is high. In addition, as discussed above, error induced due tolag between blood glucose and interstitial glucose is minimized whenglucose rate-of-change is low.

FIG. 12 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure. Referring to FIG.12, in the embodiment shown, when the calibration start event isdetected (1210), it is determined whether the current or an anticipatedor estimated glucose level is within a predetermined threshold level(1220). In certain embodiments, the threshold level is a higher thanaverage glucose level. As described above, a higher than average glucoselevel may be favorable in certain embodiments for calibration becausesome errors may be proportionally less when the glucose level is high.In one aspect, if it is determined that the glucose level is not withinthe predetermined threshold, then the initiated calibration routine isnot contemporaneously executed, but rather, the scheduled calibrationfunction is delayed for a predetermined or programmed time period(1250).

Referring still to FIG. 12, if it is determined that the glucose levelis within the predetermined threshold at step 1220, it is thendetermined whether the glucose rate-of-change is within a predeterminedthreshold (1230). In certain embodiments, as described above, performingcalibration when the glucose level is fluctuating at a lowrate-of-change may minimize errors, for example, due to lag betweenblood glucose and interstitial glucose levels. In one aspect, if it isdetermined that the glucose rate-of-change is not within thepredetermined threshold, then the initiated calibration routine is notcontemporaneously executed, but rather, the scheduled calibrationfunction is delayed for a predetermined or programmed time period(1250). On the other hand, if the rate-of-change is within the thresholdthen the calibration routine is executed or performed to completion asdescribed above at step 1240. In other embodiments, the calibrationroutine may be executed if only one of the glucose level and therate-of-change of the glucose level are within the correspondingthreshold levels.

FIG. 13 is a flowchart illustrating calibration accuracy improvementroutine in another aspect of the present disclosure. Referring to FIG.13, in one aspect of the present disclosure, the calibration routine maybe configured to notify or inform the closed-loop control process oralgorithm that calibration is required (or soon to be required) (1310).It should be noted that calibration routine may also be requested orinitiated by the patient or the caregiver (e.g., health care provider(HCP)). Upon detection or determination of an impending calibrationstart event (1310), whether by user initiation or automatic initiation(i.e. at a predetermined time interval or in response to an event), theclosed-loop control routine may be configured to modify the glucosecontrol target to a higher value (1320) (balancing with a value that maybe too high as to be detrimental to the patient).

Referring again to FIG. 13, once the calibration start event is detected(1330) the calibration routine, using the modified glucose controltarget, may be configured to determine if the current glucose level iswithin a target threshold, such as the target set by the modifiedglucose control target (1340) and only request or execute thecalibration function (1350) if the glucose level is within the targetthreshold. If it is determined that the glucose level is not within thepredetermined threshold, then the initiated calibration routine is notcontemporaneously executed, but rather, the calibration function isdelayed for a predetermined or programmed time period (1370). At thispoint, in certain embodiments, the routing may wait a predeterminedamount of time and then the routine is restarted. Once the calibrationfunction is executed (1350), the glucose control target may be resetback to normal glucose control target settings (1360).

In addition, the closed-loop control routine in one aspect may beconfigured to switch to a control target of maintaining a low rate ofchange of glucose, where the control target may be configured toincorporate the desired glucose threshold or range.

In one embodiment, control algorithm may be programmed or configured tomaintain multiple control targets for optimal calibration glucoseprofile and euglycemic management. In one aspect, euglycemic managementis configured as a higher priority over optimal calibration profile forthe safety of the patient, in the control algorithm.

In the case where a model-based control algorithm is implemented, avector of state estimates x(t) are provided that accounts for plasmainsulin, plasma glucose, and other relevant states, the state observermay be realized in the form of a Kalman Filter or other types of stateobservers, and configured to use the analyte sensor data as its sourceof measurement, in addition to the insulin delivery or dosinginformation. One example of a model-based control algorithm includes aLinear Quadratic (LQ) controller, where the objective function governsthe tradeoff between minimizing tracking error and maximizing controleffort efficiency. Then, the relative weights under normal operation andwhen calibration is near can be appropriately adjusted or modified.

For example, consider the following truth model:

İ ₁(t)=−k _(a) I ₁(t)+u _(sc)(t)

İ ₂(t)=−k _(a) I ₂(t)+k _(a) I ₁(t)

İ(t)=−k _(e) I(t)+k _(e) /V I ₂(t)

{dot over (r)} ₁(t)=−k _(M) r ₁(t)+k _(a1) I(t)

{dot over (r)} ₂(t)=−k _(b2) r ₂(t)+k _(a2) I(t)

{dot over (r)} ₃(t)=−k _(b3) r ₃(t)+k _(a3) I(t)

ġ _(b)(t)=−[r ₁(t)+k ₃₁]g _(b)(t)−F _(R) +k ₁₂ g ₁(t)+k ₁₃ g ₂(t)+EGP(r₃)+g _(m)(t)

ġ ₂(t)=−[r ₂(t)+k ₁₃]g ₂ +r ₁(t)g _(h)(t)

ġ _(i)(t)=−k ₀₂ g _(i)(t)+[k ₂₁+[k _(i)[I(t)−I _(b)]]]g _(h)(t)−F _(a)

where, in addition to Equations 1 and 2 above, other glucosecompartments g_(b) and g₂ as well as effective insulin compartments r₁,r₂, and r₃ have been included. In the case where the model for thecontrol algorithm is configured to perform a local linearization atevery time step:

$\begin{matrix}{{\overset{.}{x}(t)} = {{{A(t)}{x(t)}} + {{Bu}(t)}}} & (7) \\{{u(t)} = {u_{sc}(t)}} & \; \\{{y(t)} = {{y_{CGM}(t)} = {S\left\lbrack {{g_{i}(t)} + {v_{i}(t)}} \right\rbrack}}} & \; \\{{x(t)} = \begin{bmatrix}{I_{1} - I_{1\; t}} \\{I_{2} - I_{2\; t}} \\{I_{3} - I_{3\; t}} \\{r_{1} - r_{1\; t}} \\{r_{2} - r_{2\; t}} \\{r_{3} - r_{3\; t}} \\{g_{b} - g_{bt}} \\{g_{2} - g_{2\; t}} \\{g_{i} - g_{it}}\end{bmatrix}} & \;\end{matrix}$

It is to be noted that the states have been defined as the differencebetween the physiologically meaningful states of the truth model andtheir corresponding targets.

Further, an LQ optimal control is determined such that the objectivefunction J is minimized:

$\begin{matrix}{J = {\int_{t}^{t + t_{p}}{\left\lbrack {\left\lbrack {{x^{T}(t)}{{Qx}(t)}} \right\rbrack + \left\lbrack {{u^{T}(t)}{{Ru}(t)}} \right\rbrack} \right\rbrack{dt}}}} & (8) \\{{Q = \begin{bmatrix}q_{1,1} & \ldots & q_{1,9} \\\vdots & \ddots & \vdots \\q_{9,1} & \ldots & q_{9,9}\end{bmatrix}},\mspace{31mu}{R = \left\lbrack r_{sc} \right\rbrack}} & \;\end{matrix}$

where t_(p) is a finite future horizon in which the controller must beoptimized for, Q is a positive semidefinite matrix that penalizes linearcombinations of the states x, and R is a positive definite matrix thatpenalizes the control action.

In particular, the distinction between controlling for optimalcalibration and controlling for optimal glucose regulation, using thisLQ framework as an example, is described below. In the case ofcontrolling for optimal glucose regulation, for a given desired strictplasma glucose target of 100 mg/dL, the quantity g_(bt) is set to 100mg/dL, so that when the objective function in Equation 8 is evaluated,any deviation of g_(b) from this value will contribute to an increase inJ.

If other states do not need to be regulated at any specific level, thenthe corresponding targets I_(1t), I_(2t), and so on, can be set to anyarbitrary real value (such as zero), and Q must be tuned such that onlyq_(7,7) (which corresponds to the penalty for g_(b)) be left nonzero.The relative magnitude between q_(7,7) and r_(sc) then determinesaggressive target tracking and conservative control action.

In the case of controlling for optimal calibration, a combination ofstrict plasma glucose target and zero glucose rate is obtained, which,in one aspect may be approximated by setting the rate of change of theglucose rates to zero. As a result, the corresponding targets for theglucose compartments can be estimated as follows:

$\begin{matrix}{\begin{bmatrix}g_{bt} \\g_{2t} \\g_{it}\end{bmatrix} = {{{inv}\left( \begin{bmatrix}{- \left\lbrack {r_{1} + k_{31}} \right)} & k_{13} & k_{12} \\r_{1} & {- \left\lbrack {r_{2} + k_{13}} \right\rbrack} & 0 \\{k_{21} + \left\lbrack {k_{i}\left\lbrack {I - I_{b}} \right\rbrack} \right\rbrack} & 0 & k_{02}\end{bmatrix} \right)}\begin{bmatrix}{F_{R} - {{EGR}\left( r_{3} \right)} - g_{m}} \\0 \\F_{02}\end{bmatrix}}} & (9)\end{matrix}$

The above targets can be assigned to the glucose compartments, and as inthe optimal glucose regulation case, other targets can be set to zero.The proper state weighting matrix Q must be set such that the glucosestates track the established targets.

If calibration favors not only steady glucose but also a particularblood glucose value, then the target for blood glucose may be setexplicitly (e.g. g_(b) t=100 mg/dL), and the other glucose targets canbe derived such that the following is satisfied:

$\begin{matrix}{{\begin{bmatrix}k_{13} & k_{12} \\{- \left\lbrack {r_{2} + k_{13}} \right\rbrack} & 0 \\0 & {- k_{02}}\end{bmatrix}\begin{bmatrix}g_{2} \\g_{i}\end{bmatrix}} = \begin{bmatrix}{F_{R} - {{EGP}\left( r_{3} \right)} - g_{m} + {\left\lbrack {r_{1} + k_{31}} \right\rbrack g_{bt}}} \\{{- r_{1}}g_{bt}} \\{F_{02} - {\left\lbrack {k_{21} + \left\lbrack {k_{i}\left\lbrack {I - I_{b}} \right\rbrack} \right\rbrack} \right\rbrack g_{bt}}}\end{bmatrix}} & (10)\end{matrix}$

The targets for g₂ and g_(i) can then be computed using theleast-squares error approximation shown:

$\begin{matrix}{\begin{bmatrix}g_{2t} \\g_{it}\end{bmatrix} = {{{inv}\left( {\begin{bmatrix}k_{13} & {- \left\lbrack {r_{2} + k_{13}} \right\rbrack} & 0 \\k_{12} & 0 & {- k_{02}}\end{bmatrix}\begin{bmatrix}k_{13} & k_{12} \\{- \left\lbrack {r_{2} + k_{13}} \right\rbrack} & 0 \\0 & {- k_{02}}\end{bmatrix}} \right)}{\quad{\begin{bmatrix}k_{13} & {- \left\lbrack {r_{2} + k_{13}} \right)} & 0 \\k_{12} & 0 & {- k_{02}}\end{bmatrix}\begin{bmatrix}{F_{R} - {{EGP}\left( r_{3} \right)} - g_{m} + {\left\lbrack {r_{1} + k_{31}} \right\rbrack g_{bt}}} \\{{- r_{1}}g_{bt}} \\{F_{02} - {\left\lbrack {k_{21} + \left\lbrack {k_{i}\left\lbrack {I - I_{b}} \right\rbrack} \right\rbrack} \right\rbrack g_{bt}}}\end{bmatrix}}}}} & (11)\end{matrix}$

In the manner described above, in accordance with aspects of the presentdisclosure, one or more parameters or information of events that mayimpact the level of blood glucose or glucose measurements, if availableduring the analyte sensor calibration process, may be factored in toimprove the sensor calibration accuracy, for example, by improving theaccuracy of the sensor sensitivity determination. Events or conditionsreferred to herein include, but not limited to exercise information,meal intake information, patient health information, medicationinformation, disease information, physiological profile information, andinsulin delivery information. While the various embodiments describedabove in conjunction with the improvement of the sensor calibrationaccuracy include insulin delivery information, within the scope of thepresent disclosure, any exogenous information that are available to theand during the calibration process or routine that may have an impact onthe level of glucose may be considered.

In one aspect, the user or the patient may provide this information intoone or more components of the system 100 (FIG. 1) which includes a userinterface for entering events and/or data. Alternatively, thisinformation may be entered manually into another device and transferredelectronically to the processor(s) performing the calibrationprocess/routine. Finally, this information may be recorded by either thedevice(s) that perform the calibration process/routine, or by a separatedevice that transfers the information electronically to the device(s)that perform the calibration process/routine.

In one embodiment, the medication delivery device is configured todeliver appropriate medication based on one or more delivery profilesstored therein, and in addition, configured to record the amount ofmedication delivered with delivery time association in an electronic logor database. The medication delivery device may be configured toperiodically (automatically, or in response to one or more commands fromthe controller/another device) transfer medication deliverydata/information to the controller (or another device) electronic log ordatabase. In this manner, the analyte monitoring device including thereceiver/controller unit may be provided with software programming thatcan be executed to perform the sensor calibration routine and providedwith access to all relevant information received from the medicationdelivery unit, the analyte sensor/transmitter, user input information,as well as previously stored information.

In this manner, in one aspect of the present disclosure, the accuracy ofthe sensor sensitivity determination may be improved based on theinsulin delivery information which provides additional data to determineor anticipate future glucose values, and may help to compensate forpotential error in the sensor readings or measurements due to lag, inparticular, when the level of glucose is undergoing a rapid fluctuation.In addition, the insulin information may be used to adjust or determinethe suitable or appropriate time to perform the sensor calibrationroutine. For example, this information may be used to determine oranticipate periods of high rates of glucose change which would not be anideal condition for determining sensor sensitivity for performing sensorcalibration.

Within the scope of the present disclosure, the programming,instructions or software for performing the calibration routine, userinteraction, data processing and/or communication may be incorporated inthe analyte monitoring device, the medication delivery device, thecontrol unit, or any other component of the overall system 100 shown inFIG. 1, and further, may also be provided in multiple devices orcomponents to provide redundancy. Additionally, embodiments describedherein may also be integrated in a closed loop control system which isprogrammed to control insulin delivery so as to provide, in part,conditions that are suitable for performing sensor calibration in theclosed loop control system.

In one embodiment, a method may include detecting an analyte sensorcalibration start event, determining one or more parameters associatedwith a calibration routine corresponding to the detected calibrationstart event, and executing the calibration routine based on the one ormore determined parameters, wherein the one or more determinedparameters includes a medication delivery information.

Detecting the calibration start event may include monitoring an elapsedtime period from initial analyte sensor placement.

Detecting the calibration start event may be based at least in part on apredetermined schedule.

The predetermined schedule may include approximately once every twentyfour hours.

The determined one or more parameters may include an amount of insulindose delivered, a time period of the delivered insulin dose, an insulinsensitivity parameter, an insulin on board information, an exerciseinformation, a meal intake information, an activity information, or oneor more combinations thereof.

The medication delivery information may include an insulin deliveryamount and time information relative to the detected calibration startevent.

Executing the calibration routine may include delaying the calibrationroutine by a predetermined time period.

The predetermined time period may include approximately 1-2 hours.

The calibration routine may not be executed when one of the one or moredetermined parameters deviates from a predetermined threshold level.

The predetermined threshold level may be dynamically modified based on avariation in the corresponding one or more determined parameters.

The predetermined threshold level may be user defined.

Executing the calibration routine may include determining a referencemeasurement value.

Determining the reference measurement value may include prompting for ablood glucose measurement, and receiving data corresponding to themeasured blood glucose level.

Executing the calibration routine may include determining a sensitivityvalue associated with the analyte sensor.

Executing the calibration routine may include calibrating the analytesensor.

In another embodiment, a device may include one or more processors and amemory operatively coupled to the one or more processors, the memory forstoring instructions which, when executed by the one or more processors,causes the one or more processors to detect an analyte sensorcalibration start event, to determine one or more parameters associatedwith a calibration routine corresponding to the detected calibrationstart event, and to execute the calibration routine based on the one ormore determined parameters, wherein the one or more determinedparameters includes a medication delivery information.

The analyte sensor may include a glucose sensor.

The medication delivery information may include information associatedwith insulin dose administered.

Furthermore, an output unit may be operatively coupled to the one ormore processors for outputting one or more data or signals associatedwith the calibration start event or the calibration routine.

In yet another embodiment, a method may include initializing an analytesensor, receiving a data stream from the initialized analyte sensor,detecting a calibration start event associated with the initializedanalyte sensor, determining one or more parameters associated withinsulin dose administration, and executing a calibration routine basedon the one or more determined parameters.

In yet another embodiment, a method may include detecting an impendingglucose sensor calibration start event, modifying a medical treatmentprofile to a higher than average target glucose level upon detection ofthe impending glucose sensor calibration start event, determining one ormore parameters associated with a calibration routine corresponding tothe detected impending calibration start event, wherein the one or moredetermined parameters includes a current glucose level, executing thecalibration routine based on the one or more determined parameters, andresetting the medical treatment profile to an average target glucoselevel.

The calibration routine may be executed only if the current glucoselevel is above a predetermined threshold.

The predetermined threshold may be higher than the average glucoselevel.

In one aspect, the method may include delaying execution of thecalibration routine until the current glucose level is above thepredetermined threshold.

In another aspect, the method may include outputting one or more data orsignals associated with the calibration routine.

The medical treatment profile may include insulin dose administrationinformation.

Various other modifications and alterations in the structure and methodof operation of this disclosure will be apparent to those skilled in theart without departing from the scope and spirit of the presentdisclosure. Although the present disclosure has been described inconnection with specific embodiments, it should be understood that thepresent disclosure as claimed should not be unduly limited to suchspecific embodiments. It is intended that the following claims definethe scope of the present disclosure and that structures and methodswithin the scope of these claims and their equivalents be coveredthereby.

1. An analyte monitoring device, comprising: an analyte sensor includingat least a portion configured to detect an analyte level in bodily fluidunder a skin surface of a user; one or more processors; a memoryoperatively coupled to the one or more processors, the memory storinginstructions therein which, when executed by the one or more processors,cause the one or more processors to: record an event associated with theuser having occurred at a first time; detect a subsequent determinationof the analyte level using a sensitivity determination to occur at asecond time, determine a time difference between the first time and thesecond time; alter the sensitivity determination if the time differenceis within a threshold; and determine the analyte level at the secondtime using the altered sensitivity determination.
 2. The device of claim1, wherein the event comprises an insulin delivery.
 3. The device ofclaim 1, wherein the event comprises a meal event.
 4. The device ofclaim 1, wherein the event comprises an activity event.
 5. The device ofclaim 1, wherein the event comprises an exercise event.
 6. The device ofclaim 1, wherein the sensitivity determination comprises determining alag factor.
 7. The device of claim 1, wherein altering the sensitivitydetermination comprises applying a correction factor to the sensitivitydetermination.
 8. The device of claim 1, wherein altering thesensitivity determination is based at least in part on an insulin doseamount.
 9. The device of claim 1, wherein altering the sensitivitydetermination is based at least in part on an insulin sensitivity. 10.The device of claim 1, wherein altering the sensitivity determination isbased at least in part on an insulin action time.
 11. The device ofclaim 7, wherein the correction factor comprises a predetermined valueor parameter based in part on a physiological condition of the patient.12. The device of claim 7, wherein the correction factor comprises afactor that is configured to be updated based on a variation of thedetected analyte level.
 13. The device of claim 1, wherein the thresholdcomprises a predetermined threshold.
 14. The device of claim 1, whereinthe threshold is dynamically determined by the one or more processorsbased at least in part on information associated with the recordedevent.
 15. An analyte monitoring method using an analyte sensorincluding at least a portion configured to detect an analyte level inbodily fluid under a skin surface of a user, the method comprising:recording, by one or more processors, an event associated with the userhaving occurred at a first time; detecting, by the one or moreprocessors, a subsequent determination of the analyte level using asensitivity determination to occur at a second time, determining, by theone or more processors, a time difference between the first time and thesecond time; altering, by the one or more processors, the sensitivitydetermination if the time difference is within a threshold; anddetermining, by the one or more processors, the analyte level at thesecond time using the altered sensitivity determination.
 16. The methodof claim 15, wherein the event comprises an insulin delivery.
 17. Themethod of claim 15, wherein the event comprises a meal event.
 18. Themethod of claim 15, wherein the event comprises an activity event. 19.The method of claim 15, wherein the event comprises an exercise event.20. The method of claim 15, wherein the sensitivity determinationcomprises determining a lag factor.
 21. The method of claim 15, whereinaltering the sensitivity determination comprises applying a correctionfactor to the sensitivity determination.
 22. The method of claim 15,wherein altering the sensitivity determination is based at least in parton an insulin dose amount.
 23. The method of claim 15, wherein alteringthe sensitivity determination is based at least in part on an insulinsensitivity.
 24. The method of claim 15, wherein altering thesensitivity determination is based at least in part on an insulin actiontime.
 25. The method of claim 21, wherein the correction factorcomprises a predetermined value or parameter based in part on aphysiological condition of the patient.
 26. The method of claim 21,wherein the correction factor comprises a factor that is configured tobe updated based on a variation of the detected analyte level.
 27. Themethod of claim 15, wherein the threshold comprises a predeterminedthreshold.
 28. The method of claim 15, wherein the threshold isdynamically determined by the one or more processors based at least inpart on information associated with the recorded event.